{"id":"https://openalex.org/W4415089521","doi":"https://doi.org/10.48550/arxiv.2509.12086","title":"SAQ: Pushing the Limits of Vector Quantization through Code Adjustment and Dimension Segmentation","display_name":"SAQ: Pushing the Limits of Vector Quantization through Code Adjustment and Dimension Segmentation","publication_year":2025,"publication_date":"2025-09-15","ids":{"openalex":"https://openalex.org/W4415089521","doi":"https://doi.org/10.48550/arxiv.2509.12086"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2509.12086","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.12086","pdf_url":"https://arxiv.org/pdf/2509.12086","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2509.12086","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100427615","display_name":"Hui Li","orcid":"https://orcid.org/0000-0002-9593-0117"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Hui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077816312","display_name":"Shiyuan Deng","orcid":"https://orcid.org/0009-0007-7883-3458"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Deng, Shiyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100367774","display_name":"Xiao Yan","orcid":"https://orcid.org/0000-0002-2122-915X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan, Xiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119959385","display_name":"Xiangyu Zhi","orcid":"https://orcid.org/0009-0001-0122-9240"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhi, Xiangyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5016082884","display_name":"James Cheng","orcid":"https://orcid.org/0000-0001-6313-6288"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, James","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9153000116348267,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9153000116348267,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/vector-quantization","display_name":"Vector quantization","score":0.8219000101089478},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.666700005531311},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.6208999752998352},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.578000009059906},{"id":"https://openalex.org/keywords/nearest-neighbor-search","display_name":"Nearest neighbor search","score":0.459199994802475},{"id":"https://openalex.org/keywords/linde\u2013buzo\u2013gray-algorithm","display_name":"Linde\u2013Buzo\u2013Gray algorithm","score":0.44600000977516174},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.3693999946117401},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.36079999804496765}],"concepts":[{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.8219000101089478},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.666700005531311},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.6208999752998352},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.578000009059906},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5647000074386597},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5619999766349792},{"id":"https://openalex.org/C116738811","wikidata":"https://www.wikidata.org/wiki/Q608751","display_name":"Nearest neighbor search","level":2,"score":0.459199994802475},{"id":"https://openalex.org/C93372532","wikidata":"https://www.wikidata.org/wiki/Q6552455","display_name":"Linde\u2013Buzo\u2013Gray algorithm","level":3,"score":0.44600000977516174},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.3693999946117401},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.36079999804496765},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3440999984741211},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.33009999990463257},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32679998874664307},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3255999982357025},{"id":"https://openalex.org/C40567965","wikidata":"https://www.wikidata.org/wiki/Q1820283","display_name":"Learning vector quantization","level":3,"score":0.3174999952316284},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29660001397132874},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.2865999937057495},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.2761000096797943},{"id":"https://openalex.org/C13336665","wikidata":"https://www.wikidata.org/wiki/Q125977","display_name":"Vector space","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C37404715","wikidata":"https://www.wikidata.org/wiki/Q380679","display_name":"Dynamic programming","level":2,"score":0.2630000114440918},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.25450000166893005},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.25440001487731934},{"id":"https://openalex.org/C125583679","wikidata":"https://www.wikidata.org/wiki/Q755673","display_name":"Search algorithm","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2509.12086","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.12086","pdf_url":"https://arxiv.org/pdf/2509.12086","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2509.12086","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.12086","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2509.12086","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.12086","pdf_url":"https://arxiv.org/pdf/2509.12086","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Approximate":[0],"Nearest":[1],"Neighbor":[2],"Search":[3],"(ANNS)":[4],"plays":[5],"a":[6,24,64,75,138,155],"critical":[7],"role":[8],"in":[9,51,187],"applications":[10],"such":[11],"as":[12],"search":[13],"engines,":[14],"recommender":[15],"systems,":[16],"and":[17,36,55,124,148,173,190],"RAG":[18],"for":[19,27],"LLMs.":[20],"Vector":[21],"quantization":[22,56,129,188],"(VQ),":[23],"crucial":[25],"technique":[26,79,141],"ANNS,":[28],"is":[29,118],"commonly":[30],"used":[31],"to":[32,80,102,120,142,158,184,198],"reduce":[33],"space":[34,111],"overhead":[35],"accelerate":[37],"distance":[38],"computations.":[39],"However,":[40],"despite":[41],"significant":[42],"research":[43],"advances,":[44],"state-of-the-art":[45,175],"VQ":[46,66],"methods":[47,169],"still":[48],"face":[49],"challenges":[50],"balancing":[52],"encoding":[53,192],"efficiency":[54],"accuracy.":[57],"To":[58,70,131],"address":[59],"these":[60],"limitations,":[61],"we":[62],"propose":[63],"novel":[65],"method":[67],"called":[68],"SAQ.":[69],"improve":[71],"accuracy,":[72],"SAQ":[73,98,136,181],"employs":[74],"new":[76],"dimension":[77,93,122,146],"segmentation":[78,123],"strategically":[81],"partition":[82],"PCA-projected":[83],"vectors":[84,153],"into":[85],"segments":[86,94],"along":[87],"their":[88],"dimensions.":[89],"By":[90],"prioritizing":[91],"leading":[92],"with":[95],"larger":[96],"magnitudes,":[97],"allocates":[99],"more":[100],"bits":[101],"high-impact":[103],"segments,":[104],"optimizing":[105],"the":[106,109],"use":[107],"of":[108],"available":[110],"quota.":[112],"An":[113],"efficient":[114],"dynamic":[115],"programming":[116],"algorithm":[117],"developed":[119],"optimize":[121],"bit":[125],"allocation,":[126],"ensuring":[127],"minimal":[128],"error.":[130],"speed":[132,193],"up":[133,183],"vector":[134],"encoding,":[135],"devises":[137],"code":[139],"adjustment":[140],"first":[143],"quantize":[144],"each":[145],"independently":[147],"then":[149],"progressively":[150],"refine":[151],"quantized":[152],"using":[154],"coordinate-descent-like":[156],"approach":[157],"avoid":[159],"exhaustive":[160],"enumeration.":[161],"Extensive":[162],"experiments":[163],"demonstrate":[164],"SAQ's":[165],"superiority":[166],"over":[167,195],"classical":[168],"(e.g.,":[170,177],"PQ,":[171],"PCA)":[172],"recent":[174],"approaches":[176],"LVQ,":[178],"Extended":[179,199],"RabitQ).":[180],"achieves":[182],"80%":[185],"reduction":[186],"error":[189],"accelerates":[191],"by":[194],"80x":[196],"compared":[197],"RabitQ.":[200]},"counts_by_year":[],"updated_date":"2026-06-23T13:55:30.953635","created_date":"2025-10-12T00:00:00"}